from sklearn.metrics import roc_curve
from sklearn.model_selection import cross_val_predict
from sklearn.metrics import roc_curve
"""
The code below is created by author of this submission to Kaggle. It serves an inspiration.
Source: https://www.kaggle.com/code/janiobachmann/credit-fraud-dealing-with-imbalanced-datasets
"""
log_reg_pred = cross_val_predict(lr_best, X_train_under, y_train_under, cv=5,
method="decision_function")
best_rfc_pred=cross_val_predict(best_rfc, X_train_under, y_train_under, cv=5)
gbc_best_pred = cross_val_predict(gbc_best, X_train_under, y_train_under, cv=5)
xgb_best_pred = cross_val_predict(xgb_best, X_train_under, y_train_under, cv=5)
lgbm_best_pred = cross_val_predict(lgbm_best, X_train_under, y_train_under, cv=5)
tree_pred_pred = cross_val_predict(best_dtc, X_train_under, y_train_under, cv=5)
log_fpr, log_tpr, log_thresold = roc_curve(y_train_under, log_reg_pred)
rfc_fpr, rfc_tpr, rfc_threshold = roc_curve(y_train_under, best_rfc_pred)
gbc_fpr, gbc_tpr, gbc_threshold = roc_curve(y_train_under, gbc_best_pred)
xgb_fpr, xgb_tpr, xgb_threshold = roc_curve(y_train_under, xgb_best_pred)
lgbm_fpr, lgbm_tpr, lgbm_threshold = roc_curve(y_train_under, lgbm_best_pred)
tree_fpr, tree_tpr, tree_threshold = roc_curve(y_train_under, tree_pred_pred)
def graph_roc_curve_multiple(log_fpr, log_tpr, rfc_fpr, rfc_tpr, gbc_fpr, gbc_tpr, xgb_fpr, xgb_tpr, lgbm_fpr, lgbm_tpr, tree_fpr, tree_tpr):
plt.figure(figsize=(16,8))
plt.title('ROC Curve \n Top 6 Classifiers', fontsize=18)
plt.plot(log_fpr, log_tpr, label='Logistic Regression Classifier Score: {:.4f}'.format(roc_auc_score(y_train_under, log_reg_pred)))
plt.plot(rfc_fpr, rfc_tpr, label='Random Forest Classifier Score: {:.4f}'.format(roc_auc_score(y_train_under, best_rfc_pred)))
plt.plot(gbc_fpr, gbc_tpr, label='Gradient Boosting Classifier Score: {:.4f}'.format(roc_auc_score(y_train_under, gbc_best_pred)))
plt.plot(xgb_fpr, xgb_tpr, label='XGBoost Classifier Score: {:.4f}'.format(roc_auc_score(y_train_under, gbc_best_pred)))
plt.plot(lgbm_fpr, lgbm_tpr, label='Light GBM Classifier Score: {:.4f}'.format(roc_auc_score(y_train_under, lgbm_best_pred)))
plt.plot(tree_fpr, tree_tpr, label='Decision Tree Classifier Score: {:.4f}'.format(roc_auc_score(y_train_under, tree_pred_pred)))
plt.plot([0, 1], [0, 1], 'k--')
plt.axis([-0.01, 1, 0, 1])
plt.xlabel('False Positive Rate', fontsize=16)
plt.ylabel('True Positive Rate', fontsize=16)
plt.annotate('Minimum ROC Score of 50% \n (This is the minimum score to get)', xy=(0.5, 0.5), xytext=(0.6, 0.3),
arrowprops=dict(facecolor='#6E726D', shrink=0.05),
)
plt.legend()
graph_roc_curve_multiple(log_fpr, log_tpr, rfc_fpr, rfc_tpr, gbc_fpr, gbc_tpr, xgb_fpr, xgb_tpr, lgbm_fpr, lgbm_tpr, tree_fpr, tree_tpr)
plt.show()
